Which databases and targets does CocoIndex support?

It provides built-in connectors for a wide range of targets including PostgreSQL, SQLite, LanceDB, Qdrant, and local file systems.

What makes CocoIndex different from standard ETL tools?

Unlike procedural ETL, CocoIndex is declarative and incremental. It functions similarly to React for data, ensuring that only changed data is reprocessed and synced to your targets.

Can I use this for LLM-based data extraction?

Yes, CocoIndex is optimized for LLM workflows. It includes function memoization that skips expensive LLM calls if the input content or the underlying code hasn't changed.

Does CocoIndex require a custom domain-specific language (DSL)?

No, CocoIndex is entirely Python-native. You can use standard Python types, dataclasses, and Pydantic models to define your data structures and transformations.

CocoIndex Pipeline Builder

Name: CocoIndex Pipeline Builder
Author: cocoindex-io

bycocoindex-io

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Data Science & ML

Builds incremental data processing and vector embedding pipelines using declarative Python-native states and automatic change detection.

About

CocoIndex v1 is a Python-native framework that streamlines the creation of data pipelines by treating them like reactive systems. It allows developers to declare the desired output state based on inputs, while the engine automatically handles change detection, incremental updates, and function memoization to avoid redundant operations. This skill is particularly useful for building ETL workflows, RAG (Retrieval-Augmented Generation) indexing systems, and knowledge graph extraction where performance and data consistency are critical.

Key Features

Built-in function memoization for expensive LLM or embedding calls
40 GitHub stars
Pre-configured connectors for PostgreSQL, Qdrant, and local files
Native support for Python types like Pydantic and dataclasses
Declarative target state management for SQL and Vector DBs
Incremental data updates via automatic change detection

Use Cases

Building RAG pipelines with automated vector embedding updates
Extracting structured data from files using LLM-based workflows
Transforming and syncing large document sets across multiple databases

About

Key Features

Built-in function memoization for expensive LLM or embedding calls
40 GitHub stars
Pre-configured connectors for PostgreSQL, Qdrant, and local files
Native support for Python types like Pydantic and dataclasses
Declarative target state management for SQL and Vector DBs
Incremental data updates via automatic change detection

Use Cases

Building RAG pipelines with automated vector embedding updates
Extracting structured data from files using LLM-based workflows
Transforming and syncing large document sets across multiple databases